Entering the second decade of the Industrie 4.0 vision, the production sector is facing challenges in taking full advantage of global digitalization. Production research has focused on sophisticated mathematical models ranging from molecular materials modeling to production control to supply chain logistics. These models help simulate and control the related physical system but the variety of individual situations and behaviors is captured only as statistical uncertainty. The emergence of data-driven methods adds statistical or AI models learned from real-time production data to Digital Twins, and ideally allows for continuous synchronization (twinning) between physical and virtual system. However, the complexity of today’s production systems precludes Digital Twins covering more than just a few system perspectives, especially if realtime performance is required. To achieve better performance and more precise context adaptation, the interdisciplinary research cluster “Internet of Production” at RWTH Aachen University is exploring the concept of Digital Shadows. We conceptualize Digital Shadows as a generalization of compact views on dynamic processes, whose defining “query” combines condensed measurement data with efficient simplified mathematical models. Their small size makes Digital Shadows amenable to dynamic function allocation in hybrid cloud–edge settings. In addition to showing the similarities and differences to the traditional view concept, we also present a conceptual embedding of Digital Shadows in the context of large distributed system architectures, and sovereign data exchange in international Data Space communities. Two production use case experiences demonstrate that Digital Shadows can be valuable carriers of deep and reusable engineering knowledge for technical and ecological progress.

The Industrial Internet-of-Things (IIoT) promises significant improvements for the manufacturing industry by facilitating the integration of manufacturing systems by Digital Twins. However, ecological and economic demands also require a cross-domain linkage of multiple scientific perspectives from material sciences, engineering, operations, business, and ergonomics, as optimization opportunities can be derived from any of these perspectives. To extend the IIoT to a trueInternet of Production, two concepts are required: first, a complex, interrelated network of Digital Shadows which combine domain-specific models with data-driven AI methods; and second, the integration of a large number of research labs, engineering, and production sites as a World Wide Lab which offers controlled exchange of selected, innovation-relevant data even across company boundaries. In this article, we define the underlying Computer Science challenges implied by these novel concepts in four layers:Smart human interfacesprovide access to information that has been generated bymodel-integrated AI. Given the large variety of manufacturing data, newdata modelingtechniques should enable efficient management of Digital Shadows, which is supported by aninterconnected infrastructure. Based on a detailed analysis of these challenges, we derive a systematized research roadmap to make the vision of the Internet of Production a reality.

In the production industry, the volume, variety, and velocity of data as well as the number of deployed protocols increase exponentially due to the influences of the Internet-of-Things (IoT) advances. While hundreds of isolated solutions exist to utilize these data, e.g., optimizing processes or monitoring machine conditions, the lack of a unified data handling and exchange mechanism hinders the implementation of approaches to improve the quality of decisions and processes in such an interconnected environment. The vision of anInternet of Productionpromises the establishment of aWorldwide Lab, where data from every process in the network can be utilized, even interorganizational and across domains. While numerous existing approaches consider interoperability from an interface and communication system perspective, fundamental questions of data and information interoperability remain insufficiently addressed. In this article, we identifytenkey issues, derived from three distinctive real-world use cases that hinder large-scale data interoperability for industrial processes. Based on these issues, we derive a set offivekey requirements for future (IoT) data layers, building upon the FAIR data principles. We propose to address them by creatingFactDAG, a conceptual data layer model for maintaining a provenance-based, directed acyclic graph of facts, inspired by successful distributed version-control and collaboration systems. Eventually, such a standardization should greatly shape the future of interoperability in an interconnected production industry.

The action programming language GOLOG has been found useful for the control of autonomous agents such as mobile robots. In scenarios like these, tasks are often open-ended so that the respective control programs are non-terminating. Before deploying such programs on a robot, it is often desirable to verify that they meet certain requirements. For this purpose, Claßen and Lakemeyer recently introduced algorithms for the verification of temporal properties of GOLOG programs. However, given the expressiveness of GOLOG, their verification procedures are not guaranteed to terminate. In this paper, we show how decidability can be obtained by suitably restricting the underlying base logic, the effect axioms for primitive actions, and the use of actions within GOLOG programs. Moreover, we show that dropping any of these restrictions immediately leads to undecidability of the verification problem.